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Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification

Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the...

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Detalles Bibliográficos
Autores principales: Nawa, Norberto E., Ando, Hiroshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688375/
https://www.ncbi.nlm.nih.gov/pubmed/26778992
http://dx.doi.org/10.3389/fnhum.2015.00689
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author Nawa, Norberto E.
Ando, Hiroshi
author_facet Nawa, Norberto E.
Ando, Hiroshi
author_sort Nawa, Norberto E.
collection PubMed
description Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., “the month you were born is an odd number”), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6 to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful.
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spelling pubmed-46883752016-01-15 Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification Nawa, Norberto E. Ando, Hiroshi Front Hum Neurosci Neuroscience Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of where and how information regarding different mental states is represented in the human brain, bringing new insights into how these states come to fruition, and providing a promising complement to the mass-univariate approach. Here, we employed MVPA to classify whole-brain activity patterns occurring in single fMRI scans, in order to retrieve binary answers from experiment participants. Five healthy volunteers performed two types of mental task while in the MRI scanner: counting down numbers and recalling positive autobiographical events. Data from these runs were used to train individual machine learning based classifiers that predicted which mental task was being performed based on the voxel-based brain activity patterns. On a different day, the same volunteers reentered the scanner and listened to six statements (e.g., “the month you were born is an odd number”), and were told to countdown numbers if the statement was true (yes) or recall positive events otherwise (no). The previously trained classifiers were then used to assign labels (yes/no) to the scans collected during the 24-second response periods following each one of the statements. Mean classification accuracies at the single scan level were in the range of 73.6 to 80.8%, significantly above chance for all participants. When applying a majority vote on the scans within each response period, i.e., the most frequent label (yes/no) in the response period becomes the answer to the previous statement, 5.0 to 5.8 sentences, out of 6, were correctly classified in each one of the runs, on average. These results indicate that binary answers can be retrieved from whole-brain activity patterns, suggesting that MVPA provides an alternative way to establish basic communication with unresponsive patients when other techniques are not successful. Frontiers Media S.A. 2015-12-23 /pmc/articles/PMC4688375/ /pubmed/26778992 http://dx.doi.org/10.3389/fnhum.2015.00689 Text en Copyright © 2015 Nawa and Ando. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Nawa, Norberto E.
Ando, Hiroshi
Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title_full Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title_fullStr Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title_full_unstemmed Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title_short Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification
title_sort retrieving binary answers using whole-brain activity pattern classification
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4688375/
https://www.ncbi.nlm.nih.gov/pubmed/26778992
http://dx.doi.org/10.3389/fnhum.2015.00689
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